This data set is output from WRF model. The data include ‘LU_INDEX’ (land use category), ‘ZNU’(eta values on half (mass) levels), ‘ZNW’(eta values on full (w) levels)，’ZS’(depths of centers of soil layers), ‘DZS’ (thicknesses of soil layers), ‘VAR_SSO’ (variance of subgrid-scale orography), ‘U’(x-wind component), ‘V’(y-wind component),’W’(z-wind component),’T’(perturbation potential temperature (theta-t0)), ‘Q2’ ('QV at 2 M), ‘T2’ (TEMP at 2 M), ‘TH2’ ('POT TEMP at 2 M), ‘PSFC’ (SFC pressure), ‘U10’ (U at 10 M), ‘V10’ (V at 10 M), ‘QVAPOR’ (Water vapor mixing ratio), ‘QLOUD’ (Cloud water mixing ratio),’QRAIN’ (Rain water mixing ratio), ‘QICE’ (Ice mixing ratio), ‘QSNOW’ (Snow mixing ratio), ‘SHDMAX’ (annual max veg fraction), ‘SHDMIN’ (annual min veg fraction), ‘SNOALB’ (annual max snow albedo in fraction), ‘TSLB’ (soil temperature), ‘SMOIS’ (soil moisture), ‘GRDFLX’ (ground heat flux), ‘LAI’ (Leaf area index),’ HGT’ (Terrain Height), ‘TSK’ (surface skin temperature), ‘SWDOWN’ (downward short wave flux at ground surface), ‘GLW’ (downward long wave flux at ground surface), ‘HFX’ (upward heat flux at the surface), ‘QFX’ (upward moisture flux at the surface), ‘LH’ (latent heat flux at the surface), ‘SNOWC’ (flag indicating snow coverage (1 for snow cover)), and so on. The data is in netCDF format with a spatial resolution of 10 km.
CHEN Xuelong
In April 2014 and may 2016, 21 Lakes (7 non thermal lakes and 14 thermal lakes) were collected in the source area of the Yellow River (along the Yellow River) respectively. The abundance of hydrogen and oxygen allogens was measured by Delta V advantage dual inlet / hdevice system in inno tech Alberta laboratory in Victoria, Canada. The isotope abundance was expressed in the form of δ (‰) (relative to the average seawater abundance in Vienna) ）Test error: δ 18O: 0.1 ‰, δ D: 1 ‰. The data also includes Lake area and lake basin area extracted from Landsat 2017 image data in Google Earth engine.
WAN Chengwei
The matching data of water and soil resources in the Qinghai Tibet Plateau, the potential evapotranspiration data calculated by Penman formula from the site meteorological data (2008-2016, national meteorological data sharing network), the evapotranspiration under the existing land use according to the influence coefficient of underlying surface, and the rainfall data obtained by interpolation from the site rainfall data in the meteorological data, are used to calculate the evapotranspiration under the existing land use according to the different land types of land use According to the difference, the matching coefficient of water and soil resources is obtained. The difference between the actual rainfall and the water demand under the existing land use conditions reflects the matching of water and soil resources. The larger the value is, the better the matching is. The spatial distribution of the matching of soil and water resources can pave the way for further understanding of the agricultural and animal husbandry resources in the Qinghai Tibet Plateau.
DONG Lingxiao
This dataset includes data recorded by the Heihe integrated observatory network obtained from an observation system of Meteorological elements gradient of Daman Superstation from January 1 to December 31, 2018. The site (100.372° E, 38.856° N) was located on a cropland (maize surface) in the Daman irrigation, which is near Zhangye city, Gansu Province. The elevation is 1556 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (AV-14TH;3, 5, 10, 15, 20, 30, and 40 m, towards north), wind speed and direction profile (windsonic; 3, 5, 10, 15, 20, 30, and 40 m, towards north), air pressure (CS100; 2 m), rain gauge (TE525M; 2.5 m, 8 m in west of tower), four-component radiometer (PIR&PSP; 12 m, towards south), two infrared temperature sensors (IRTC3; 12 m, towards south, vertically downward), photosynthetically active radiation (LI190SB; 12 m, towards south, vertically upward; another four photosynthetically active radiation, PQS-1; two above the plants (12 m) and two below the plants (0.3 m), towards south, each with one vertically downward and one vertically upward), soil heat flux (HFP01SC; 3 duplicates with G1 below the vegetation; G2 and G3 between plants, -0.06 m), a TCAV averaging soil thermocouple probe (TCAV; -0.02, -0.04 m), soil temperature profile (AV-10T; 0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), soil moisture profile (CS616; -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_5 m, Ta_10 m, Ta_15 m, Ta_20 m, Ta_30 m, and Ta_40 m; RH_3 m, RH_5 m, RH_10 m, RH_15 m, RH_20 m, RH_30 m, and RH_40 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, Ws_10 m, Ws_15 m, Ws_20 m, Ws_30 m, and Ws_40 m) (m/s), wind direction (WD_3 m, WD_5 m, WD_10 m, WD_15 m, WD_20 m, WD_30m, and WD_40 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), average soil temperature (TCAV, ℃), soil heat flux (Gs_1, below the vegetation; Gs_2, and Gs_3, between plants) (W/m^2), soil temperature (Ts_0 cm, Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_80 cm, Ts_120 cm, and Ts_160 cm) (℃), soil moisture (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_80 cm, Ms_120 cm, and Ms_160 cm) (%, volumetric water content), above the plants photosynthetically active radiation of upward and downward (PAR_U_up and PAR_U_down) (μmol/ (s m-2)), and below the plants photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day.The meterological data during September 17 and November 7 and TCAV data after November 7 were wrong because the malfunction of datalogger. The missing data were denoted by -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2018-6-10 10:30. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) for data processing) in the Citation section.
LI Xin, CHE Tao, XU Ziwei, REN Zhiguo, TAN Junlei
This data set includes the daily values of temperature, air pressure, relative humidity, wind speed, precipitation, total radiation, etc. observed at Namuco station from January 1, 2017 to December 31, 2018.
WANG Junbo, WU Guangjian
The dataset records the Ali Desert Environment Integrated Observation and Research Station, the meteorological dataset for 2017-2018, and the time resolution of the data is days. It includes the following basic meteorological parameters: temperature (1.5 meters from the ground, once every half hour, unit: Celsius), relative humidity (1.5 meters from the ground, half an hour, unit: %), wind speed (1.5 meters from the ground, half an hour) , unit: m / s), wind direction (1.5 meters from the ground, once every half hour, unit: degrees), air pressure (1.5 meters from the ground, once every half hour, unit: hPa), precipitation (24 hours, unit: mm ), water vapor pressure (unit: Kpa), evaporation (unit: mm), downward short-wave radiation (unit: W/m2), upward short-wave radiation (unit: W/m2), downward long-wave radiation (unit: W/m2) ), upward long-wave radiation (unit: W/m2), net radiation (unit: W/m2), surface albedo (unit: %). Data collection location: Observation Field of Ali Desert Environment Comprehensive Observation and Research Station, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Longitude: 79°42'5"; Latitude: 33°23'30"; Altitude: 4264 meters.
ZHAO Huabiao
Based on the WRF model, using ERA5 reanalysis data as the initial and boundary fields, the high-resolution low-level atmospheric structure and the earth atmosphere exchange data set of the Qinghai Tibet Plateau are preliminarily obtained by the method of dynamic downscaling. The time range of this data set is from August 1 to August 31, 2014, with a time resolution of 1 hour, a horizontal range of 25 °N-40 °N, 70oE-105oE, and a horizontal resolution of 0.05 °. The data format is NetCDF, and one file is output every hour. The file is named after the date. The lower atmospheric structure data includes temperature, relative humidity, water vapor mixing ratio, potential height, meridional wind and latitudinal wind meteorological elements, with 34 isobaric surfaces in the vertical direction; the surface air exchange data set includes the upward / downward short wave radiation, upward / downward long wave radiation, surface sensible heat and flux, 2m air temperature and water vapor mixing ratio, 10m wind, etc. The data set can provide data support for the study of weather process and climate environment in the Tibetan Plateau.
Ma Shupo
（1）This data set provides atmospheric temperature (2 meters above land surface), vapor content, precipitation, press, wind velocity and solar radiation (since 2015). （2）All data were generated using AWS (auto weather station), and been calculated their daily average. （3）All data are presented here are raw data, after being evaluated regarding their quality. （4）This data set could be used in background description for related studies.
Da Wei, WANG Xiaodan
This dataset includes data recorded by the Cold and Arid Research Network of Lanzhou university obtained from an observation system of Meteorological elements gradient of Dunhuang Station from January 1 to December 31, 2018. The site (93.708° E, 40.348° N) was located on a wetland in the Dunhuang west lake, Gansu Province. The elevation is 990 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (4m and 8 m, towards north), wind speed and direction profile (windsonic; 4m and 8 m, towards north), air pressure (1 m), rain gauge (4 m), infrared temperature sensors (4 m, towards south, vertically downward), soil heat flux (-0.05 and -0.1m ), soil soil temperature/ moisture/ electrical conductivity profile (below the vegetation in the south of tower, -0.05 and -0.2 m), photosynthetically active radiation (4 m, towards south), four-component radiometer (4 m, towards south), sunshine duration sensor(4 m, towards south). The observations included the following: air temperature and humidity (Ta_4 m, Ta_8 m; RH_2 m, RH_4 m, RH_8 m) (℃ and %, respectively), wind speed (Ws_4 m, Ws_8 m) (m/s), wind direction (WD_4 m, WD_8 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), soil heat flux (Gs_0.05m, Gs_0.1m) (W/m^2), soil temperature (Ts_0.05m, Ts_0.2m) (℃), soil moisture (Ms_0.05m, Ms_0.2m) (%, volumetric water content), soil conductivity (Ec_0.05m, Ec_0.2m)(μs/cm), sun time(h). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The data were missing during Jan. 23 to Jan. 24 because of collector failure; the data during Mar. 17 and May 24 were wrong because of the tower body tilt; The air humidity data were rejected due to program error. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2018-6-10 10:30.
ZHAO Changming, ZHANG Renyi
The China Meteorological Forcing Dataset (CMFD) is a high spatial-temporal resolution gridded near-surface meteorological dataset that was developed specifically for studies of land surface processes in China. The dataset was made through fusion of remote sensing products, reanalysis dataset and in-situ observation data at weather stations. Its record starts from January 1979 and keeps extending (currently up to December 2018) with a temporal resolution of three hours and a spatial resolution of 0.1°. Seven near-surface meteorological elements are provided in CMFD, including 2-meter air temperature, surface pressure, specific humidity, 10-meter wind speed, downward shortwave radiation, downward longwave radiation and precipitation rate.
YANG Kun, HE Jie, WENJUN TANG , LU Hui, QIN Jun , CHEN Yingying, LI Xin
This data set contains the data of meteorological elements observed in the pass station upstream of heihewen meteorological observation network on January 1, 2015 and December 31, 2015.The site is located in da dong shu pass, qilian county, qinghai province.The longitude and latitude of the observation point are 100.2421E, 38.0142N, and the altitude is 4148m.Data including two observation points, all in pass observatory, located about 10 m, a set of continuous observation in 2015 (30 min output), another set for September 18, 2015 in 10 m high pass new stations (10 min), specific include: air temperature, relative humidity sensors at 5 m, toward the north (two sets of observation, 10 min and 30 min output);The barometer is installed in the skid-proof box on the ground (two groups of observation, 10min and 30min output respectively);The tipping bucket rain gauge is installed at 10m;The wind speed and direction sensor is mounted at 10m, facing due north (two groups, 10min and 30min output respectively).The four-component radiometer consists of two observation points, one is installed at the meteorological tower 6m, facing due south (10min output), and the other is installed on the support 1.5m above the ground (30min output).Two infrared thermometers are installed at 6m, facing south, with the probe facing vertically downward;The soil temperature probe was buried at 0cm on the surface and 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm underground (the two groups were observed for 10min and 30min respectively).The soil moisture probe was buried in the ground at 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm (the two groups were observed for 10min and 30min respectively).The soil heat flow plate was buried 6cm underground (observed in two groups, 10min (3 heat flow plates) and 30min (2 heat flow plates)). Observation projects are: air temperature and humidity (Ta_5m, RH_5m) (unit: c, percentage), pressure (Press) (unit: hundred mpa), precipitation (Rain) (unit: mm), wind speed (WS_10m) (unit: m/s), wind (WD_10m) (unit: degrees), the radiation of four component (DR, UR, DLR_Cor, ULR_Cor, Rn) (unit: watts per square meter), the surface radiation temperature (IRT_1, IRT_2) (unit:C), soil heat flux (Gs_1, Gs_2, Gs_3) (unit: wattage/m2), soil temperature (Ts_0cm, Ts_4cm, Ts_10cm, Ts_20cm, Ts_40cm, Ts_80cm, Ts_120cm, Ts_160cm) (unit: water content by volume, percentage). Processing and quality control of observation data :(1) 144 or 48 data per day (every 10min or 30min) should be ensured.The four-component long-wave radiation output of 30min was between January 1, 2015 and January 1, 2015.The observation data was lost between 5.24 and 7.12 after 30min due to the collector problem.(2) eliminate the moments with duplicate records;(3) data that obviously exceeds the physical significance or the range of the instrument is deleted;(4) the part marked with red letters in the data is questionable data;(5) the format of date and time is uniform, and the date and time are in the same column.For example, the time is: 2015-9-10 10:30;(6) naming rules: AWS+ site name. For information of hydrometeorological network or site, please refer to Li et al. (2013), and for data processing, please refer to Liu et al. (2011).
CHE Tao, LIU Shaomin, LI Xin, XU Ziwei, ZHANG Yang, TAN Junlei
The data set contains the meteorological element observation data of ebao station in the upper reaches of heihe hydrometeorological observation network on January 1, 2015 and December 31, 2016.The station is located in ebao town, qilian county, qinghai province.The longitude and latitude of the observation point are 100.9151E, 37.9492N, and the altitude is 3294m.The air temperature and relative humidity sensor is set up at 5m, facing due north.The barometer is installed in the anti-skid box on the ground;The tipping bucket rain gauge is installed at 10m;The wind speed and direction sensor is mounted at 10m, facing due north;The four-component radiometer is installed at 6m, facing due south;Two infrared thermometers are installed at 6m, facing south, with the probe facing vertically downward;The soil temperature probe is buried at the surface of 0cm and underground of 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm, 2m south of the meteorological tower.The soil moisture probe is buried underground at 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm, 2m south of the meteorological tower.The soil heat flow plates (3 pieces) are successively buried 6cm underground, 2m south of the meteorological tower. Observation projects are: air temperature and humidity (Ta_5m, RH_5m) (unit: c, percentage), pressure (Press) (unit: hundred mpa), precipitation (Rain) (unit: mm), wind speed (WS_10m) (unit: m/s), wind (WD_10m) (unit: degrees), the radiation of four component (DR, UR, DLR_Cor, ULR_Cor, Rn) (unit: watts per square meter), the surface radiation temperature (IRT_1, IRT_2) (unit:C), soil heat flux (Gs_1, Gs_2, Gs_3) (unit: wattage/m2), soil temperature (Ts_0cm, Ts_4cm, Ts_10cm, Ts_20cm, Ts_40cm, Ts_80cm, Ts_120cm, Ts_160cm) (unit: water content by volume, percentage). Processing and quality control of observation data :(1) 144 data per day (every 10min) should be ensured.The four-component radiation and infrared temperature were between October 11, 2015 and November 5, 2015.The instrument of the observation tower was re-adjusted between 11.1 and 11.5, and the data was missing;(2) eliminate the moments with duplicate records;(3) data that obviously exceeds the physical significance or the range of the instrument is deleted;(4) the part marked with red letters in the data is questionable data;(5) the format of date and time is uniform, and the date and time are in the same column.For example, the time is: 2015-9-10 10:30;(6) naming rules: AWS+ site name. For information of hydrometeorological network or site, please refer to Li et al. (2013), and for data processing, please refer to Liu et al. (2011).
CHE Tao, LIU Shaomin, LI Xin, XU Ziwei, ZHANG Yang, TAN Junlei
The data set contains the data of the meteorological element gradient observation system of the upper reaches of the heihe hydrological and meteorological observation network's arou super station on January 1, 2015 and December 31, 2017.Site is located in qilian county, qinghai province, arou township grass daban village, the underlying surface is alpine grassland.The longitude and latitude of the observation point are 100.4643E,38.0473N, and the altitude is 3033m.The air temperature, relative humidity and wind speed sensors are installed at 1m, 2m, 5m, 10m, 15m and 25m, respectively. There are 6 floors in total, facing due north.Wind direction sensor is mounted at 10m, facing due north;The barometer is installed at 2m;The tilting rain gauge is installed on the 40m observation tower of the super station in aru.The four-component radiometer is installed at 5m, facing due south;Two infrared thermometers are mounted at 5m, facing due south, with the probe facing down vertically;The photosynthetic effective radiometer was installed at 5m, facing south, and the probe direction was vertical upward.Part of the soil sensor is buried 2m away from the south of the tower, and the soil heat flow plate (self-calibration) (3 pieces) are all buried 6cm underground.Mean soil temperature sensor (tcavr) was buried 2cm and 4cm underground.The soil temperature probe is buried at the surface 0cm and underground 2cm, 4cm, 6cm, 10cm, 15cm, 20cm, 30cm, 40cm, 60cm, 80cm, 120cm, 160cm, 200cm, 240cm, 280cm and 320cm. There are three duplicates in the two layers of 4cm and 10cm.The soil moisture sensor was buried in the ground at 2cm, 4cm, 6cm, 10cm, 15cm, 20cm, 30cm, 40cm, 60cm, 80cm, 120cm, 160cm, 200cm, 240cm, 280cm and 320cm respectively, and there were three replications in the two layers of 4cm and 10cm. Observation items include: wind speed (WS_1m, WS_2m, WS_5m, WS_10m, WS_15m, WS_25m) (unit: m/s), wind direction (WD_10m) (unit: degrees), air temperature and humidity (Ta_1m, Ta_2m, Ta_5m, Ta_10m, Ta_15m, Ta_25m and RH_1m, RH_2m, RH_5m, RH_10m, RH_5m) (unit: Celsius, percentage), air pressure (Press) (unit:Hundred mpa), precipitation (Rain) (unit: mm), the radiation of four component (DR, UR, DLR_Cor, ULR_Cor, Rn) (unit: watts per square meter), the surface radiation temperature (IRT_1, IRT_2) (unit: c), photosynthetic active radiation (PAR) (unit: second micromoles/m2), the average soil temperature (TCAV) (unit: c), soil heat flux (Gs_1, Gs_2, Gs_3) (unit:W/m2), soil moisture (Ms_2cm, Ms_4cm_1, Ms_4cm_2, Ms_4cm_3, Ms_6cm, Ms_10cm_1, Ms_10cm_2, Ms_10cm_3, Ms_15cm, Ms_20cm, Ms_30cm, Ms_60cm, Ms_80cm, Ms_120cm, Ms_160cm, Ms_280cm, Ms_320cm) (unit:Soil temperature (Ts_0cm, Ts_2cm, Ts_4cm_1, Ts_4cm_2, Ts_4cm_3, Ts_6cm, Ts_10cm_1, Ts_10cm_2, Ts_15cm, Ts_20cm, Ts_30cm, Ts_60cm, Ts_80cm, Ts_120cm, Ts_160cm, Ts_280cm, Ts_320cm) (unit:Degrees Celsius. Processing and quality control of observation data :(1) 144 data per day (every 10min) should be ensured.The data of soil temperature and humidity and soil heat flux were missing between September 9, 2015 and September 19, 2015 and between September 30 and October 20, 2015 due to power supply problems.(2) eliminate the moments with duplicate records;(3) data that obviously exceeds the physical significance or the range of the instrument is deleted;(4) the part marked with red letters in the data is questionable data;(5) the format of date and time is uniform, and the date and time are in the same column.For example, the time is: June 10, 2015 10:30;(6) naming rules: AWS+ site name. For information of hydrometeorological network or site, please refer to Li et al. (2013), and for data processing, please refer to Liu et al. (2011).
CHE Tao, LIU Shaomin, LI Xin, XU Ziwei, ZHANG Yang, TAN Junlei
The data set contains the meteorological element observation data of the upper reaches of the heihe hydrological meteorological observation network of daxun station on January 1, 2015 and December 31, 2017.The site is located in the western side of qilian county, qinghai province.The longitude and latitude of the observation point are 98.9406°E, 38.8399°N and 3739m above sea level.The air temperature and relative humidity sensor is set up at 5m, facing due north.The barometer is installed in the anti-skid box on the ground;The tipping bucket rain gauge is installed at 10m;The wind speed and direction sensor is mounted at 10m, facing due north;The four-component radiometer is installed at 6m, facing due south;Two infrared thermometers are installed at 6m, facing south, with the probe facing vertically downward;The soil temperature probe is buried at the surface of 0cm and underground of 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm, 2m south of the meteorological tower.The soil moisture probe is buried underground at 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm, 2m south of the meteorological tower.The soil heat flow plate (3 pieces) is buried in the underground 6cm successively and is 2m south of the meteorological tower. Observation projects are: air temperature and humidity (Ta_5m, RH_5m) (unit: c, percentage), pressure (Press) (unit: hundred mpa), precipitation (Rain) (unit: mm), wind speed (WS_10m) (unit: m/s), wind (WD_10m) (unit: degrees), the radiation of four component (DR, UR, DLR_Cor, ULR_Cor, Rn) (unit: watts per square meter), the surface radiation temperature (IRT_1, IRT_2) (unit:C), soil heat flux (Gs_1, Gs_2, Gs_3) (unit: wattage/m2), soil temperature (Ts_0cm, Ts_4cm, Ts_10cm, Ts_20cm, Ts_40cm, Ts_80cm, Ts_120cm, Ts_160cm) (unit: water content by volume, percentage). Processing and quality control of observation data :(1) 144 data per day (every 10min) should be ensured.(2) eliminate the moments with duplicate records;(3) data that obviously exceeds the physical significance or the range of the instrument is deleted;(4) the part marked with red letters in the data is questionable data;(5) the format of date and time is uniform, and the date and time are in the same column.For example, the time is: 2015-9-10 10:30;(6) naming rules: AWS+ site name. For information of hydrometeorological network or site, please refer to Li et al. (2013), and for data processing, please refer to Liu et al. (2011).
CHE Tao, LIU Shaomin, LI Xin, XU Ziwei, ZHANG Yang, TAN Junlei
The data set contains the meteorological element observation data of jingyangling station in the upper reaches of heihe hydrometeorological observation network on January 1, 2015 and December 31, 2017.The site is located in pass, jingyangling mountain, qilian county, qinghai province.The longitude and latitude of the observation point are 101.1160E, 37.8384N and 3750m above sea level.The air temperature and relative humidity sensor is set up at 5m, facing due north.The barometer is installed in the anti-skid box on the ground;The tipping bucket rain gauge is installed at 10m;The wind speed and direction sensor is mounted at 10m, facing due north;The four-component radiometer is installed at 6m, facing due south;Two infrared thermometers are installed at 6m, facing south, with the probe facing vertically downward;The soil temperature probe is buried at the surface of 0cm and underground of 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm, 2m south of the meteorological tower.The soil moisture probe is buried underground at 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm, 2m south of the meteorological tower.The soil heat flow plates (3 pieces) are successively buried 6cm underground, 2m south of the meteorological tower. Observation projects are: air temperature and humidity (Ta_5m, RH_5m) (unit: c, percentage), pressure (Press) (unit: hundred mpa), precipitation (Rain) (unit: mm), wind speed (WS_10m) (unit: m/s), wind (WD_10m) (unit: degrees), the radiation of four component (DR, UR, DLR_Cor, ULR_Cor, Rn) (unit: watts per square meter), the surface radiation temperature (IRT_1, IRT_2) (unit:Soil temperature (Ts_0cm, Ts_4cm, Ts_10cm, Ts_20cm, Ts_40cm, Ts_80cm, Ts_120cm, Ts_160cm) (unit: percent). Processing and quality control of observation data :(1) 144 data per day (every 10min) should be ensured.(2) eliminate the moments with duplicate records;(3) data that obviously exceeds the physical significance or the range of the instrument is deleted;(4) the part marked with red letters in the data is questionable data;(5) the format of date and time is uniform, and the date and time are in the same column.For example, the time is: 2015-9-10 10:30;(6) naming rules: AWS+ site name. For information of hydrometeorological network or site, please refer to Li et al. (2013), and for data processing, please refer to Liu et al. (2011).
CHE Tao, LIU Shaomin, LI Xin, XU Ziwei, ZHANG Yang, TAN Junlei
1) Data content : total column water / precipitable water; 2) Data sources and processing methods: ECMWF-interm monthly mean analysis; 3) Data quality description: time resolution: monthly, spatial resolution: 0.7°*0.7°; 4) Data application results and prospects: this data can be used for analysis of water resources in the air.
YAN Hongru
This dataset is derived from the global atmospheric reanalysis dataset, ERA-Interim, based on the 4-dimensional variational analysis (4D-Var) released by the European Centre for Medium-Range Weather Forecasts (ECMWF). ERA-Interim represents a major undertaking by ECMWF (European Centre for Medium-Range Weather Forecasts) to produce a reanalysis with an improved atmospheric model and assimilation system which replaces those used in ERA-40, particularly for the data-rich 1990s and 2000s, and to be continued as an ECMWF Climate Data Assimilation System (ECDAS) until superseded by a new reanalysis. Through systematic increases in computing power, 4-dimensional variational assimilation (4D-Var) became feasible and part of ECMWF operations since 1997. Enhanced computing power also allowed horizontal resolution to be increased from T159 to T255, and the latest Integrated Forecasting System(IFS CY31r1 and CY31r2) to be used, taking advantage of improved model physics. ERA-interim retains the same 60 model levels used for ERA-40 with the highest level being 0.1 hPa. Besides, data assimilation of ERA-Interim also benefits from quality control that draws on experience from ERA-40 and JRA-25, variational bias correction of satellite radiance data, and more extensive use of radiances with an improved fast radiative transfer model. In addition, ERA-Interim uses the new ERS (European Remote Sensing Satellite) altimeter wave heights, EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) reprocessed winds and clear-sky radiances, GOME (Global Ozone Monitoring Experiment) ozone data from the Rutherford Appleton Laboratory, and CHAMP (CHAllenging Minisatellite Payload), GRACE (Gravity Recovery and Climate Experiment), and COSMIC (Constellation Observing System for Meteorology, Ionosphere and Climate) GPS radio occultation measurements processed and archived by UCAR (University Corporation for Atmospheric Research).
DENG Chuangwu
Shergyla Mountain meteorological data, Record the surface near Linzhi(1.2-1.5m) conventional meteorological observation.The dataset records the meteorological data at the eastern slope of Shergyla Mountain from 2005 to 2016, and North-facing slope from 2005 to 2012.Including daily average data of temperature, relative humidity, precipitation. Data collected near the eastern slope timberline of Shergyla Mountain, Latitude:29°39′25.2″N; Longitude:94°42′25.62″E; Altitude:4390m, and collected near the north-facing slope of Shergyla Mountain, Latitude:29°35′50.9″N; Longitude:94°36′42.7″E; Altitude:4390m. Collector: Campbell Co CR1000. Collection time interval：30min. Digital automatic data collection, daily average value of artificial calculation. It includes the following basic meteorological parameters: North-facing slope data: Wind speed，Unit m/s Temperature，Unit ℃ Relative Humidity，Unit % Atmospheric pressure，Unit hPa Global radiation，Unit w/m2 Soil heat flux，Unit w/m2 Soil temperature，Unit ℃ Soil moisture，Unit % Precipitation，Unit mm Thickness of snow, Unit cm Ecology station data: Temperature，Unit ℃ Relative Humidity，Unit % Atmospheric pressure，Unit hPa Wind speed，Unit m/s Precipitation，Unit mm Snow Depth，Unit cm Radiation，Unit w/m2 Soil moisture content，Unit % Soil heat flux，Unit w/m2
Luo Lun
This data set includes the daily values of temperature, pressure, relative humidity, wind speed, wind direction, precipitation, radiation, and water vapor pressure observed from 22 international exchange stations in Sri Lanka from January 1, 2008 to October 1, 2018. The data was downloaded from the NCDC of NOAA. The data set processing method is that the original data is quality-controlled to form a continuous time series. It satisfies the accuracy of the original meteorological observation data of the National Weather Service and the World Meteorological Organization (WMO), and eliminates the systematic error caused by the failure of the tracking data and the sensor. The meteorological site information contained in this dataset is as follows: LATITUDE LONGITUDE ELEVATION  COUNTRY  STATION NAME +09.800  +080.067   +0015.0   SRI LANKA  KANKASANTURAI +09.650  +080.017   +0003.0   SRI LANKA  JAFFNA +09.267  +080.817   +0002.0   SRI LANKA  MULLAITTIVU +08.983  +079.917   +0003.0   SRI LANKA  MANNAR +08.750  +080.500   +0098.0   SRI LANKA  VAVUNIYA +08.539  +081.182   +0001.8   SRI LANKA  CHINA BAY +08.301  +080.428   +0098.8   SRI LANKA  ANURADHAPURA +08.117  +080.467   +0117.0   SRI LANKA  MAHA ILLUPPALLAMA +08.033  +079.833   +0002.0   SRI LANKA  PUTTALAM +07.706  +081.679   +0006.1   SRI LANKA  BATTICALOA +07.467  +080.367   +0116.0   SRI LANKA  KURUNEGALA +07.333  +080.633   +0477.0   SRI LANKA  KANDY +07.181  +079.866   +0008.8   SRI LANKA  BANDARANAIKE INTL COLOMBO +06.900  +079.867   +0007.0   SRI LANKA  COLOMBO +06.822  +079.886   +0006.7   SRI LANKA  COLOMBO RATMALANA +06.967  +080.767   +1880.0   SRI LANKA  NUWARA ELIYA +06.883  +081.833   +0008.0   SRI LANKA  POTTUVIL +06.817  +080.967   +1250.0   SRI LANKA  DIYATALAWA +06.983  +081.050   +0667.0   SRI LANKA  BADULLA +06.683  +080.400   +0088.0   SRI LANKA  RATNAPURA +06.033  +080.217   +0013.0   SRI LANKA  GALLE +06.117  +081.133   +0020.0   SRI LANKA  HAMBANTOTA
DENG Chuangwu
This dataset is derived from the Nagqu Station of Plateau Climate and Environment (31.37N, 91.90E, 4509 a.s.l), Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences. The ground is flat, with open surrounding terrain. An uneven growth of alpine steppe, with a height of 3–20 cm. The observation time of this dataset is from January 1, 2014 to December 31, 2017. The observation elements primarily included the wind speed, air temperature, air relative humidity, air pressure, downward shortwave radiation, precipitation, evaporation, latent heat flux and CO2 flux. The precipitation , evaporation and CO2 flux data are daily cumulative values, and the other variables are daily average values. The observed data are generally continuous, but some data are missing due to power supply failure, and the missing data in this dataset are marked as NAN.
HU Zeyong, GU Lianglei, SUN Fanglei, WANG Shujin
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